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Steal My Paper Ideas! I have more ideas than time. The real problem is that publishing papers makes the list bigger, not smaller; each paper I do gives me the idea for more than one new paper. I also don’t have my own PhD students to give them to, and don’t especially need credit for more publications. So feel free to take these and run with them, just put me in the acknowledgements, and let me know when you publish so I can take the idea off this page.

As you get lower down the page, the ideas get more speculative and cover literatures I don’t know as well, so it gets more likely that the idea has already been done and/or is bad.

State Health Insurance Mandates: Most of my early work was on these laws, but many questions remain unanswered. States have passed over a hundred different types of mandated benefits, but the vast majority have zero papers focused on them. Many likely effects of the laws have also never been studied for any mandate or combination of mandates. Do they actually reduce uncompensated hospital care, as Summers (1989) predicts? Do mandates cause higher deductibles and copays, less coverage of non-mandated care, or narrower networks? How do mandates affect the income and employment of relevant providers? Can mandates be used as an instrument to determine the effectiveness of a treatment? On the identification side, redoing older papers using a dataset like MEPS-IC where self-insured firms can be used as a control would be a major advance.

Certificate of Need: Lots has been written here, as you can see from my systematic literature review and update, but many questions are unanswered. Descriptively, what is the average acceptance rate of CON applicants by state? What predicts successful vs unsuccessful CON applications? There’s a lot of variety in what types of facilities and equipment require CON in different states; AHPA lists 28 types of CON restrictions. Many of these types have been the focus of zero papers. In terms of the effects of CON, some big outcomes not addressed since 1998: hospital beds per capita, HHI, profits. My paper on how CON affects prices is more recent but the price data we used was far from ideal, you could probably do much better now. I found that CON states have higher overall Medicare spending, but this is puzzling given that Medicare prices are mostly set nationally, you could use claims data to figure out what drives this (Quantity effects? differential upcoding? Part C?). Outcomes CON may effect that I believe have zero papers: insurance premiums, hospital utilization rates, self-reported health, most types of morbidity, nursing home abuse, hospital openings and closures by local area income. On the identification side, this is one of many literatures full of old papers that could be redone in light of the new literature on staggered adoption and two-way fixed effects.

Regulation as Protectionism: How does regulation (as measured by Quantgov) affect the proportion of foreign firms in an industry?

The Expanding Workweek? Understanding Trends in Long Work Hours among U.S. Men, 1979–2006” This paper raises an interesting puzzle, that more men are working 48+ hours at the same time that average hours are falling and many more prime-age men left the labor force entirely. But it ends in 2006, doesn’t check sensitivity to its arbitrary 48hr cutoff, and doesn’t even claim to figure out why this happened. Ripe for a followup paper.

Measuring Greed: Atul Gawande argues that one major driver of variation of health care spending across the US is variation in physician greed; some towns have a “culture of money” or “entrepreneurial spirit” among physicians. How could we get a good quantitative proxy for physician greed to test this? Ideas: the number of (non-medical?) businesses the average physician has a stake in, proportion of physicians with business degrees, physician spending on cars or luxury goods, proportion of physicians taking pharma money (Sunshine Act data).

Evidence from the Introduction of Medicare” is a great paper built on weak data. There’s an AER waiting for anyone who could do the archival work to dig up the data to re-do it properly. Most importantly, can you get 1960s health spending data by payer at the state level or lower? Can you also get data on pre-Medicare public insurance programs like Kerr-Mills or Medical Assistance for the Aged?

Does cable news literally kill people by stressing them out? I suspect so but I haven’t been able to get a panel of cable news ratings by low-level geography, and don’t know of any geographic variation in the initial rollout of cable news.

Information Shocks and Entrepreneurship: an existing theory is that people become entrepreneurs when they have high ability that is unobservable to employers. I think this means that shocks to employers’ abilities to evaluate talent should affect the rate of entrepreneurship. But I don’t have great ideas for what shocks to study; bans on employers using certain types of information generally seem too minor or easily circumvented, while changes in education generally seem too gradual.

Careful Now: Has the growth of experience-rating in malpractice insurance reduced medical errors?

Testing Becker: is labor-market discrimination more common in non-profits?

Car Safety: I think that most sites rating car safety are bad and that many economists are capable of doing better using public data; I explain my full reasoning here. This project could become a paper, a potentially profitable website, or both.

Benefits Cliffs: Implicit marginal tax rates sometimes go over 100% when you consider lost subsidies as well as higher taxes. This could be trapping many people in poverty, but we don’t have a good idea of how many, because so many of the relevant subsidies operate at the state and local level. Descriptive work cataloging where all these “benefits cliffs” are and how many people they effect would be hugely valuable. You could also study how people react to benefits cliffs using the data we do have.

*If you think an idea on this page has already been done, please let me know so I can take it down*